{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DS1000C Rigol Waveform Examples\n",
    "\n",
    "**Scott Prahl**\n",
    "\n",
    "**Mar 2021**\n",
    "\n",
    "This notebook illustrates shows how to extract signals from a `.wfm` file created by a the Rigol DS1202CA scope.  \n",
    "\n",
    "*If RigolWFM is not installed, uncomment the following cell (i.e., delete the #) and run (shift-enter)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --user RigolWFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "try:\n",
    "    import RigolWFM.wfm as rigol\n",
    "except ModuleNotFoundError:\n",
    "    print('RigolWFM not installed. To install, uncomment and run the cell above.')\n",
    "    print('Once installation is successful, rerun this cell again.')\n",
    "\n",
    "repo = \"https://github.com/scottprahl/RigolWFM/raw/master/wfm/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of Rigol scopes in the DS1000C family is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C', '1000C', 'DS1000C', 'DS1000CD', 'DS1000C', 'DS1000MD', 'DS1000M', 'DS1302CA', 'DS1202CA', 'DS1102CA', 'DS1062CA']\n"
     ]
    }
   ],
   "source": [
    "print(rigol.DS1000C_scopes[:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DS1202CA\n",
    "\n",
    "We will start with a `.wfm` file from a Rigol DS1202CA scope. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now for the `.wfm` data\n",
    "\n",
    "First a textual description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading 'https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS1202CA-A.wfm?raw=true'\n",
      "    General:\n",
      "        File Model   = wfm1000c\n",
      "        User Model   = 1000C\n",
      "        Parser Model = wfm1000c\n",
      "        Firmware     = unknown\n",
      "        Filename     = DS1202CA-A.wfm\n",
      "        Channels     = [1, 2]\n",
      "\n",
      "     Channel 1:\n",
      "         Coupling =  unknown\n",
      "            Scale =   200.00 mV/div\n",
      "           Offset =  -608.00 mV\n",
      "            Probe =       1X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =   10.000 ms/div\n",
      "           Offset =   -1.600 ms\n",
      "            Delta =  100.000 µs/point\n",
      "           Points =     5120\n",
      "\n",
      "         Count    = [        1,        2,        3  ...      5119,     5120]\n",
      "           Raw    = [      198,      198,      198  ...       192,      192]\n",
      "           Times  = [-257.600 ms,-257.500 ms,-257.400 ms  ... 254.300 ms,254.400 ms]\n",
      "           Volts  = [ 40.00 mV, 40.00 mV, 40.00 mV  ...  88.00 mV, 88.00 mV]\n",
      "\n",
      "     Channel 2:\n",
      "         Coupling =  unknown\n",
      "            Scale =   500.00 mV/div\n",
      "           Offset =     0.00  V\n",
      "            Probe =       1X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =   10.000 ms/div\n",
      "           Offset =   -1.600 ms\n",
      "            Delta =  100.000 µs/point\n",
      "           Points =     5120\n",
      "\n",
      "         Count    = [        1,        2,        3  ...      5119,     5120]\n",
      "           Raw    = [       92,       92,       92  ...        77,       77]\n",
      "           Times  = [-257.600 ms,-257.500 ms,-257.400 ms  ... 254.300 ms,254.400 ms]\n",
      "           Volts  = [700.00 mV,700.00 mV,700.00 mV  ...   1.00  V,  1.00  V]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# raw=true is needed because this is a binary file\n",
    "wfm_url = \"https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS1202CA-A.wfm\" + \"?raw=true\"  \n",
    "w = rigol.Wfm.from_url(wfm_url, '1000C')\n",
    "\n",
    "description = w.describe()\n",
    "print(description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ch = w.channels[0]\n",
    "plt.subplot(211)\n",
    "plt.plot(ch.times, ch.volts, color='green')\n",
    "plt.title(\"DS1202CA-A from .wfm file\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "#plt.xlim(-0.6,0.6)\n",
    "\n",
    "plt.xticks([])\n",
    "\n",
    "ch = w.channels[1]\n",
    "plt.subplot(212)\n",
    "plt.plot(ch.times, ch.volts, color='red')\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "#plt.xlim(-0.6,0.6)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  },
  "nteract": {
   "version": "0.15.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
